{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 产生分隔的高斯分布的聚类簇\n",
    "# make_blobs 随机生成高斯分布的方法\n",
    "# centers 核心点的数组\n",
    "# num 样本数\n",
    "# std 每簇的标准差\n",
    "def create_data(centers,num=1000,std=0.7):\n",
    "    x, labels_true = make_blobs(n_samples=num, centers=centers, cluster_std=std)\n",
    "    return x, labels_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示生成的样本图像\n",
    "def plot_data(*data):\n",
    "    x,labels_true = data\n",
    "    labels = np.unique(labels_true)\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(15)\n",
    "    fig.set_figwidth(25)\n",
    "    ax = fig.add_subplot(111)\n",
    "    colors = 'rgbyckm'\n",
    "    for i, label in enumerate(labels):\n",
    "        position=labels_true==label\n",
    "        ax.scatter(x[position,0],x[position,1],label='cluster={0}'.format(label),color=colors[i%len(colors)])\n",
    "    \n",
    "    ax.legend(loc='best',framealpha=0.5)\n",
    "    ax.set_xlabel('X')\n",
    "    ax.set_ylabel('Y')\n",
    "    ax.set_title('data')\n",
    "    plt.show()\n",
    "\n",
    "x, labels_true = create_data(((1,1),(2,2),(1,2),(10,20)),1000,0.5)\n",
    "plot_data(x, labels_true)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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